Overview

Dataset statistics

Number of variables13
Number of observations10742
Missing cells23391
Missing cells (%)16.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory104.0 B

Variable types

Categorical1
Numeric11
Unsupported1

Alerts

time has a high cardinality: 283 distinct values High cardinality
SDDY is highly correlated with GZDY and 1 other fieldsHigh correlation
GZDY is highly correlated with SDDY and 1 other fieldsHigh correlation
PJDY is highly correlated with SDDY and 1 other fieldsHigh correlation
BD is highly correlated with ZZHigh correlation
ZZ is highly correlated with BDHigh correlation
SDDY is highly correlated with GZDY and 2 other fieldsHigh correlation
GZDY is highly correlated with SDDY and 2 other fieldsHigh correlation
PJDY is highly correlated with SDDY and 2 other fieldsHigh correlation
XLCS is highly correlated with SDDY and 2 other fieldsHigh correlation
SDDY is highly correlated with GZDY and 1 other fieldsHigh correlation
GZDY is highly correlated with SDDY and 1 other fieldsHigh correlation
PJDY is highly correlated with SDDY and 1 other fieldsHigh correlation
CLL is highly correlated with DJWD and 2 other fieldsHigh correlation
SDDY is highly correlated with GZDY and 3 other fieldsHigh correlation
DJWD is highly correlated with CLL and 3 other fieldsHigh correlation
FZB is highly correlated with DJWDHigh correlation
GZDY is highly correlated with CLL and 5 other fieldsHigh correlation
PJDY is highly correlated with CLL and 5 other fieldsHigh correlation
XLCS is highly correlated with SDDY and 3 other fieldsHigh correlation
BD is highly correlated with GZDY and 3 other fieldsHigh correlation
ZZ is highly correlated with BDHigh correlation
number is highly correlated with SDDYHigh correlation
CLL has 339 (3.2%) missing values Missing
SDDY has 826 (7.7%) missing values Missing
LSP has 3503 (32.6%) missing values Missing
YHLND has 10742 (100.0%) missing values Missing
DJWD has 3502 (32.6%) missing values Missing
FZB has 349 (3.2%) missing values Missing
GZDY has 826 (7.7%) missing values Missing
PJDY has 826 (7.7%) missing values Missing
XLCS has 826 (7.7%) missing values Missing
BD has 826 (7.7%) missing values Missing
ZZ has 826 (7.7%) missing values Missing
time is uniformly distributed Uniform
YHLND is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-07-06 12:44:28.620362
Analysis finished2022-07-06 12:45:00.951372
Duration32.33 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

time
Categorical

HIGH CARDINALITY
UNIFORM

Distinct283
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size84.0 KiB
2021-08-01 00:00:00
 
38
2022-02-09 00:00:00
 
38
2022-01-26 00:00:00
 
38
2022-01-27 00:00:00
 
38
2022-01-28 00:00:00
 
38
Other values (278)
10552 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-08-01 00:00:00
2nd row2021-08-02 00:00:00
3rd row2021-08-03 00:00:00
4th row2021-08-04 00:00:00
5th row2021-08-05 00:00:00

Common Values

ValueCountFrequency (%)
2021-08-01 00:00:0038
 
0.4%
2022-02-09 00:00:0038
 
0.4%
2022-01-26 00:00:0038
 
0.4%
2022-01-27 00:00:0038
 
0.4%
2022-01-28 00:00:0038
 
0.4%
2022-01-29 00:00:0038
 
0.4%
2022-01-30 00:00:0038
 
0.4%
2022-01-31 00:00:0038
 
0.4%
2022-02-01 00:00:0038
 
0.4%
2022-02-02 00:00:0038
 
0.4%
Other values (273)10362
96.5%

Length

2022-07-06T20:45:01.182017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:0010742
50.0%
2021-08-0138
 
0.2%
2021-08-1038
 
0.2%
2021-08-1638
 
0.2%
2021-08-1538
 
0.2%
2021-08-1438
 
0.2%
2021-08-1338
 
0.2%
2021-08-1238
 
0.2%
2021-08-1138
 
0.2%
2021-08-0938
 
0.2%
Other values (274)10400
48.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CLL
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct37
Distinct (%)0.4%
Missing339
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean99.83278766
Minimum99.34
Maximum99.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.0 KiB
2022-07-06T20:45:01.372240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum99.34
5-th percentile99.79
Q199.82
median99.84
Q399.85
95-th percentile99.86
Maximum99.87
Range0.53
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.03288966765
Coefficient of variation (CV)0.0003294475534
Kurtosis35.51343228
Mean99.83278766
Median Absolute Deviation (MAD)0.01
Skewness-4.725033174
Sum1038560.49
Variance0.001081730238
MonotonicityNot monotonic
2022-07-06T20:45:01.590900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
99.854100
38.2%
99.841828
17.0%
99.831175
 
10.9%
99.821072
 
10.0%
99.86650
 
6.1%
99.8466
 
4.3%
99.81429
 
4.0%
99.78245
 
2.3%
99.79161
 
1.5%
99.7759
 
0.5%
Other values (27)218
 
2.0%
(Missing)339
 
3.2%
ValueCountFrequency (%)
99.341
 
< 0.1%
99.41
 
< 0.1%
99.421
 
< 0.1%
99.451
 
< 0.1%
99.461
 
< 0.1%
99.471
 
< 0.1%
99.483
< 0.1%
99.492
< 0.1%
99.521
 
< 0.1%
99.541
 
< 0.1%
ValueCountFrequency (%)
99.8716
 
0.1%
99.86650
 
6.1%
99.854100
38.2%
99.841828
17.0%
99.831175
 
10.9%
99.821072
 
10.0%
99.81429
 
4.0%
99.8466
 
4.3%
99.79161
 
1.5%
99.78245
 
2.3%

SDDY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct68
Distinct (%)0.7%
Missing826
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean3969.681021
Minimum0
Maximum4500
Zeros28
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size84.0 KiB
2022-07-06T20:45:01.809637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3900
Q13950
median3980
Q34005
95-th percentile4065
Maximum4500
Range4500
Interquartile range (IQR)55

Descriptive statistics

Standard deviation216.6721267
Coefficient of variation (CV)0.0545817474
Kurtosis315.4257784
Mean3969.681021
Median Absolute Deviation (MAD)30
Skewness-17.34456007
Sum39363357
Variance46946.8105
MonotonicityNot monotonic
2022-07-06T20:45:02.075205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39801574
 
14.7%
4000759
 
7.1%
3950667
 
6.2%
3990622
 
5.8%
4020562
 
5.2%
4010339
 
3.2%
3960307
 
2.9%
3940297
 
2.8%
3970285
 
2.7%
3975272
 
2.5%
Other values (58)4232
39.4%
(Missing)826
 
7.7%
ValueCountFrequency (%)
028
 
0.3%
386554
 
0.5%
387054
 
0.5%
387518
 
0.2%
388063
 
0.6%
388579
0.7%
389083
0.8%
389595
0.9%
3900178
1.7%
39055
 
< 0.1%
ValueCountFrequency (%)
450010
0.1%
44004
 
< 0.1%
43502
 
< 0.1%
43001
 
< 0.1%
42502
 
< 0.1%
42003
 
< 0.1%
41951
 
< 0.1%
41751
 
< 0.1%
41701
 
< 0.1%
41651
 
< 0.1%

LSP
Real number (ℝ≥0)

MISSING

Distinct8
Distinct (%)0.1%
Missing3503
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean17.8050145
Minimum16
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.0 KiB
2022-07-06T20:45:02.309510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile16
Q117
median18
Q319
95-th percentile20
Maximum20
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.34963014
Coefficient of variation (CV)0.07580056394
Kurtosis-1.098567911
Mean17.8050145
Median Absolute Deviation (MAD)1
Skewness0.202284765
Sum128890.5
Variance1.821501516
MonotonicityNot monotonic
2022-07-06T20:45:02.743862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
181881
17.5%
161578
14.7%
171567
14.6%
201121
 
10.4%
191079
 
10.0%
16.57
 
0.1%
18.54
 
< 0.1%
17.52
 
< 0.1%
(Missing)3503
32.6%
ValueCountFrequency (%)
161578
14.7%
16.57
 
0.1%
171567
14.6%
17.52
 
< 0.1%
181881
17.5%
18.54
 
< 0.1%
191079
10.0%
201121
10.4%
ValueCountFrequency (%)
201121
10.4%
191079
10.0%
18.54
 
< 0.1%
181881
17.5%
17.52
 
< 0.1%
171567
14.6%
16.57
 
0.1%
161578
14.7%

YHLND
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing10742
Missing (%)100.0%
Memory size84.0 KiB

DJWD
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct24
Distinct (%)0.3%
Missing3502
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean24.84657459
Minimum14.5
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.0 KiB
2022-07-06T20:45:02.947031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum14.5
5-th percentile22
Q124
median26
Q326
95-th percentile26
Maximum26
Range11.5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.676662457
Coefficient of variation (CV)0.06748062802
Kurtosis6.465236126
Mean24.84657459
Median Absolute Deviation (MAD)0
Skewness-2.220096658
Sum179889.2
Variance2.811196995
MonotonicityNot monotonic
2022-07-06T20:45:03.212594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
263679
34.2%
25988
 
9.2%
24944
 
8.8%
23524
 
4.9%
24.5226
 
2.1%
23.5199
 
1.9%
25.5138
 
1.3%
22109
 
1.0%
22.593
 
0.9%
21.571
 
0.7%
Other values (14)269
 
2.5%
(Missing)3502
32.6%
ValueCountFrequency (%)
14.52
 
< 0.1%
15.53
 
< 0.1%
1610
 
0.1%
16.517
0.2%
1717
0.2%
17.525
0.2%
1827
0.3%
18.529
0.3%
1923
0.2%
19.520
0.2%
ValueCountFrequency (%)
263679
34.2%
25.5138
 
1.3%
25988
 
9.2%
24.5226
 
2.1%
24944
 
8.8%
23.5199
 
1.9%
23.43
 
< 0.1%
23524
 
4.9%
22.593
 
0.9%
22109
 
1.0%

FZB
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct68
Distinct (%)0.7%
Missing349
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean2.649146541
Minimum2.39
Maximum3.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.0 KiB
2022-07-06T20:45:03.446952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.39
5-th percentile2.51
Q12.58
median2.62
Q32.69
95-th percentile2.88
Maximum3.37
Range0.98
Interquartile range (IQR)0.11

Descriptive statistics

Standard deviation0.114738687
Coefficient of variation (CV)0.04331156667
Kurtosis4.406459292
Mean2.649146541
Median Absolute Deviation (MAD)0.05
Skewness1.572253098
Sum27532.58
Variance0.0131649663
MonotonicityNot monotonic
2022-07-06T20:45:03.743756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.61636
 
5.9%
2.6572
 
5.3%
2.58570
 
5.3%
2.59559
 
5.2%
2.62523
 
4.9%
2.57487
 
4.5%
2.63483
 
4.5%
2.56453
 
4.2%
2.64418
 
3.9%
2.65359
 
3.3%
Other values (58)5333
49.6%
ValueCountFrequency (%)
2.395
 
< 0.1%
2.4218
 
0.2%
2.437
 
0.1%
2.447
 
0.1%
2.4535
 
0.3%
2.4614
 
0.1%
2.4761
0.6%
2.4863
0.6%
2.4971
0.7%
2.590
0.8%
ValueCountFrequency (%)
3.377
 
0.1%
3.337
 
0.1%
3.2614
0.1%
3.237
 
0.1%
3.227
 
0.1%
3.29
0.1%
3.177
 
0.1%
3.127
 
0.1%
3.117
 
0.1%
3.0718
0.2%

GZDY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct59
Distinct (%)0.6%
Missing826
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean3.992251916
Minimum0
Maximum6.26
Zeros26
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size84.0 KiB
2022-07-06T20:45:03.962457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.92
Q13.97
median4
Q34.04
95-th percentile4.09
Maximum6.26
Range6.26
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.2189647892
Coefficient of variation (CV)0.05484743793
Kurtosis293.3498062
Mean3.992251916
Median Absolute Deviation (MAD)0.03
Skewness-15.97429832
Sum39587.17
Variance0.04794557889
MonotonicityNot monotonic
2022-07-06T20:45:04.196774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.99854
 
8.0%
3.98790
 
7.4%
4777
 
7.2%
4.01758
 
7.1%
3.97692
 
6.4%
4.03649
 
6.0%
4.02614
 
5.7%
4.04571
 
5.3%
3.96569
 
5.3%
3.95481
 
4.5%
Other values (49)3161
29.4%
(Missing)826
 
7.7%
ValueCountFrequency (%)
026
0.2%
1.311
 
< 0.1%
1.941
 
< 0.1%
2.161
 
< 0.1%
2.631
 
< 0.1%
2.661
 
< 0.1%
3.491
 
< 0.1%
3.853
 
< 0.1%
3.875
 
< 0.1%
3.8810
 
0.1%
ValueCountFrequency (%)
6.261
< 0.1%
6.071
< 0.1%
5.721
< 0.1%
5.31
< 0.1%
4.951
< 0.1%
4.582
< 0.1%
4.51
< 0.1%
4.451
< 0.1%
4.411
< 0.1%
4.391
< 0.1%

PJDY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct66
Distinct (%)0.7%
Missing826
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean4.007325534
Minimum0
Maximum6.31
Zeros26
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size84.0 KiB
2022-07-06T20:45:04.431097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.93
Q13.98
median4.01
Q34.05
95-th percentile4.12
Maximum6.31
Range6.31
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.2221287631
Coefficient of variation (CV)0.05543067595
Kurtosis281.3756921
Mean4.007325534
Median Absolute Deviation (MAD)0.04
Skewness-15.3303297
Sum39736.64
Variance0.04934118741
MonotonicityNot monotonic
2022-07-06T20:45:04.665410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.99769
 
7.2%
4749
 
7.0%
4.01732
 
6.8%
3.98676
 
6.3%
4.02630
 
5.9%
4.03612
 
5.7%
3.97560
 
5.2%
4.04547
 
5.1%
3.96492
 
4.6%
4.05445
 
4.1%
Other values (56)3704
34.5%
(Missing)826
 
7.7%
ValueCountFrequency (%)
026
0.2%
1.311
 
< 0.1%
2.161
 
< 0.1%
2.631
 
< 0.1%
2.641
 
< 0.1%
2.661
 
< 0.1%
3.661
 
< 0.1%
3.851
 
< 0.1%
3.873
 
< 0.1%
3.888
 
0.1%
ValueCountFrequency (%)
6.311
< 0.1%
6.221
< 0.1%
6.111
< 0.1%
5.591
< 0.1%
51
< 0.1%
4.781
< 0.1%
4.721
< 0.1%
4.661
< 0.1%
4.551
< 0.1%
4.521
< 0.1%

XLCS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct403
Distinct (%)4.1%
Missing826
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean926.9453409
Minimum0
Maximum1140
Zeros30
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size84.0 KiB
2022-07-06T20:45:04.899747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile827
Q1888
median927
Q3973
95-th percentile1037
Maximum1140
Range1140
Interquartile range (IQR)85

Descriptive statistics

Standard deviation84.32650229
Coefficient of variation (CV)0.09097246469
Kurtosis48.95670128
Mean926.9453409
Median Absolute Deviation (MAD)43
Skewness-4.63293214
Sum9191590
Variance7110.958989
MonotonicityNot monotonic
2022-07-06T20:45:05.148802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91485
 
0.8%
92079
 
0.7%
91977
 
0.7%
93277
 
0.7%
89973
 
0.7%
91173
 
0.7%
88972
 
0.7%
90272
 
0.7%
92272
 
0.7%
91272
 
0.7%
Other values (393)9164
85.3%
(Missing)826
 
7.7%
ValueCountFrequency (%)
030
0.3%
11
 
< 0.1%
91
 
< 0.1%
201
 
< 0.1%
921
 
< 0.1%
2111
 
< 0.1%
2591
 
< 0.1%
2891
 
< 0.1%
3691
 
< 0.1%
4481
 
< 0.1%
ValueCountFrequency (%)
11401
< 0.1%
11351
< 0.1%
11331
< 0.1%
11311
< 0.1%
11301
< 0.1%
11281
< 0.1%
11252
< 0.1%
11221
< 0.1%
11202
< 0.1%
11181
< 0.1%

BD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct475
Distinct (%)4.8%
Missing826
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean2.170246067
Minimum0
Maximum47.44
Zeros27
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size84.0 KiB
2022-07-06T20:45:05.366479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.13
Q11.52
median1.99
Q32.65
95-th percentile3.77
Maximum47.44
Range47.44
Interquartile range (IQR)1.13

Descriptive statistics

Standard deviation0.9713310009
Coefficient of variation (CV)0.4475672209
Kurtosis477.4931407
Mean2.170246067
Median Absolute Deviation (MAD)0.535
Skewness11.04576341
Sum21520.16
Variance0.9434839132
MonotonicityNot monotonic
2022-07-06T20:45:05.593841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.6375
 
0.7%
1.5770
 
0.7%
1.5168
 
0.6%
1.4967
 
0.6%
1.7767
 
0.6%
1.4167
 
0.6%
1.5667
 
0.6%
1.3466
 
0.6%
1.6565
 
0.6%
1.364
 
0.6%
Other values (465)9240
86.0%
(Missing)826
 
7.7%
ValueCountFrequency (%)
027
0.3%
0.231
 
< 0.1%
0.451
 
< 0.1%
0.562
 
< 0.1%
0.611
 
< 0.1%
0.621
 
< 0.1%
0.641
 
< 0.1%
0.663
 
< 0.1%
0.671
 
< 0.1%
0.682
 
< 0.1%
ValueCountFrequency (%)
47.441
< 0.1%
11.831
< 0.1%
11.161
< 0.1%
7.921
< 0.1%
7.91
< 0.1%
7.231
< 0.1%
6.861
< 0.1%
6.491
< 0.1%
6.312
< 0.1%
6.31
< 0.1%

ZZ
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1425
Distinct (%)14.4%
Missing826
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean8.595066559
Minimum0
Maximum198.63
Zeros27
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size84.0 KiB
2022-07-06T20:45:05.811289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.46
Q16.42
median7.55
Q39.64
95-th percentile15.1975
Maximum198.63
Range198.63
Interquartile range (IQR)3.22

Descriptive statistics

Standard deviation3.954363477
Coefficient of variation (CV)0.4600736305
Kurtosis543.7707539
Mean8.595066559
Median Absolute Deviation (MAD)1.38
Skewness12.87368105
Sum85228.68
Variance15.6369905
MonotonicityNot monotonic
2022-07-06T20:45:06.057988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.2536
 
0.3%
6.3134
 
0.3%
7.1934
 
0.3%
6.6533
 
0.3%
6.2433
 
0.3%
6.6932
 
0.3%
6.4232
 
0.3%
6.9231
 
0.3%
6.1630
 
0.3%
6.4430
 
0.3%
Other values (1415)9591
89.3%
(Missing)826
 
7.7%
ValueCountFrequency (%)
027
0.3%
0.271
 
< 0.1%
0.511
 
< 0.1%
0.751
 
< 0.1%
0.871
 
< 0.1%
0.911
 
< 0.1%
1.031
 
< 0.1%
1.061
 
< 0.1%
1.131
 
< 0.1%
2.791
 
< 0.1%
ValueCountFrequency (%)
198.631
< 0.1%
46.321
< 0.1%
45.881
< 0.1%
43.781
< 0.1%
43.361
< 0.1%
39.611
< 0.1%
37.351
< 0.1%
33.551
< 0.1%
31.71
< 0.1%
31.181
< 0.1%

number
Real number (ℝ≥0)

HIGH CORRELATION

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3057.497207
Minimum3039
Maximum3076
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.0 KiB
2022-07-06T20:45:06.290893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3039
5-th percentile3040
Q13048
median3057
Q33067
95-th percentile3075
Maximum3076
Range37
Interquartile range (IQR)19

Descriptive statistics

Standard deviation10.97217162
Coefficient of variation (CV)0.003588612148
Kurtosis-1.203465514
Mean3057.497207
Median Absolute Deviation (MAD)10
Skewness0.0007505223767
Sum32843635
Variance120.3885501
MonotonicityIncreasing
2022-07-06T20:45:06.508651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
3039283
 
2.6%
3040283
 
2.6%
3061283
 
2.6%
3062283
 
2.6%
3063283
 
2.6%
3064283
 
2.6%
3065283
 
2.6%
3066283
 
2.6%
3067283
 
2.6%
3068283
 
2.6%
Other values (28)7912
73.7%
ValueCountFrequency (%)
3039283
2.6%
3040283
2.6%
3041283
2.6%
3042283
2.6%
3043283
2.6%
3044283
2.6%
3045283
2.6%
3046283
2.6%
3047283
2.6%
3048283
2.6%
ValueCountFrequency (%)
3076283
2.6%
3075283
2.6%
3074283
2.6%
3073283
2.6%
3072283
2.6%
3071283
2.6%
3070283
2.6%
3069283
2.6%
3068283
2.6%
3067283
2.6%

Interactions

2022-07-06T20:44:57.438976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:34.617745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:36.963608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:39.166205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:41.467108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:43.717025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:46.018865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:48.528568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:50.757262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:52.989203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:55.242652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:57.626429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:34.977037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:37.213547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:39.369286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:41.654570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:43.920070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:46.217526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:48.731647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:50.957003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:53.176620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:55.440084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:57.829508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:35.175296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:37.416620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:39.556743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:41.873677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:44.107526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:46.432002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:48.934722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:51.142775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:53.364076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:55.626361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:58.016968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:35.364795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:37.619690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:39.884793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:42.076790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:44.326226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:46.666323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:49.137799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:51.333303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:53.557845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:55.813376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:58.220050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:35.556925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:37.822775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:40.080996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:42.279868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:44.513719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:46.884985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:49.356461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:51.536381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:53.885888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:56.011207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:58.407495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:35.751919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:38.010234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:40.279842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:42.498563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:44.716790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:47.134926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:49.543951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:51.739497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:54.088972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:56.215094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:58.610574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:35.946915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:38.197651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:40.471325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:42.701643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:44.919834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:47.371325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:49.747032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:51.942538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:54.276429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:56.407969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:58.813652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:36.160386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:38.416382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:40.683440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:42.904723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:45.114528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:47.582821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:49.950108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:52.161271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:54.463880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:56.611044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:59.001107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:36.347124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:38.603844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:40.881914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:43.123382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:45.310406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:47.778708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:50.147283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:52.364349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:54.651349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:56.814128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:59.204185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:36.534573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:38.791297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:41.082764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:43.342119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:45.506847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:48.028650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:50.349238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:52.551769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:54.823135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:57.017202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:59.407223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:36.770596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:38.978717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:41.267633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:43.529575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:45.710591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:48.278627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:50.558686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:52.770467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:55.041868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-06T20:44:57.220275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-07-06T20:45:06.711728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-06T20:45:07.024152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-06T20:45:07.274093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-06T20:45:07.524034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-06T20:44:59.766549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-06T20:45:00.047728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-07-06T20:45:00.459902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-07-06T20:45:00.752672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

timeCLLSDDYLSPYHLNDDJWDFZBGZDYPJDYXLCSBDZZnumber
02021-08-01 00:00:0099.813890.0NaNNaNNaN2.623.913.96889.03.2919.293039
12021-08-02 00:00:0099.823890.0NaNNaNNaN2.623.913.94891.02.8315.703039
22021-08-03 00:00:0099.823890.0NaNNaNNaN2.603.944.00847.02.9612.503039
32021-08-04 00:00:0099.863890.0NaNNaNNaN2.603.943.99875.02.4110.053039
42021-08-05 00:00:0099.863890.0NaNNaNNaN2.603.923.93867.03.7114.953039
52021-08-06 00:00:0099.823890.0NaNNaNNaN2.603.933.94853.02.6920.293039
62021-08-07 00:00:0099.823890.0NaNNaNNaN2.603.934.00864.02.5421.773039
72021-08-08 00:00:0099.823890.0NaNNaNNaN2.603.943.94828.03.0220.593039
82021-08-09 00:00:0099.823890.0NaNNaNNaN2.603.923.92829.03.2312.793039
92021-08-10 00:00:0099.823890.0NaNNaNNaN2.603.933.93822.02.8717.333039

Last rows

timeCLLSDDYLSPYHLNDDJWDFZBGZDYPJDYXLCSBDZZnumber
107322022-05-13 00:00:0099.843985.017.0NaN25.02.643.993.99980.01.567.663076
107332022-05-14 00:00:0099.843985.017.0NaN25.02.643.973.97965.01.948.673076
107342022-05-15 00:00:0099.843985.016.0NaN25.02.643.994.00991.02.198.993076
107352022-05-16 00:00:0099.843985.016.0NaN26.02.644.004.00997.02.496.863076
107362022-05-17 00:00:0099.843985.019.0NaN25.02.643.993.99917.01.526.843076
107372022-05-18 00:00:0099.843985.018.0NaN26.02.643.983.98924.01.536.463076
107382022-05-19 00:00:0099.843985.017.0NaN26.02.623.993.99944.02.168.183076
107392022-05-20 00:00:0099.84NaN17.0NaN26.02.62NaNNaNNaNNaNNaN3076
107402022-05-21 00:00:0099.863985.020.0NaN25.52.623.973.97881.01.376.783076
107412022-05-22 00:00:0099.863985.018.0NaN25.52.623.973.97883.01.579.763076